Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Abstract: Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-distribution ...

Abstract: When it comes to the marriage of graph neural networks (GNNs) and model extraction attacks, the deployment of GNNs within Machine Learning as a Service (MLaaS) through a publicly ...

This paper proposes a novel method that integrates a Graph Convolutional Network (GCN) with a Particle Filter (PF) for vocal melody extraction. The approach models pitch transition probabilities using ...

This project demonstrates a complete data engineering pipeline that transforms unstructured email archives into a queryable knowledge graph. It showcases skills in: Why I Built This: I wanted to ...